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2011, ACM Transactions on Computer Systems
Large-scale network services can consist of tens of thousands of machines running thousands of unique software configurations spread across hundreds of physical networks. Testing such services for complex performance problems and configuration errors remains a difficult problem. Existing testing techniques, such as simulation or running smaller instances of a service, have limitations in predicting overall service behavior at such scales. Testing large services should ideally be done at the same scale and configuration as the target deployment, which can be technically and economically infeasible. We present DieCast , an approach to scaling network services in which we multiplex all of the nodes in a given service configuration as virtual machines across a much smaller number of physical machines in a test harness. We show how to accurately scale CPU, network, and disk to provide the illusion that each VM matches a machine in the original service in terms of both available computing...
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Modern scientific applications often require provisioning of computing and networking infra-structures tailored to particular application needs which might not be clearly defined. In addition, allo-cated resources of an actual physical infrastructure might be underutilized by applications (e.g., a pro-cess does not utilize provided CPU or network connection fully). Traditional virtualization technolo-gies can solve the problem partially, however, lead to noticeable overheads. In this paper we consider creation of virtual topologies to test distributed software behaviour without the need to construct real network topologies. We evaluate an approach to create dedicated computing environments configured for particular applications based on light-weight virtualization also known as containers. We investi-gate available capabilities to model and create dynamic container-based virtual infrastructures sharing a common set of physical resources, and evaluate their performance on a set of te...
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